Data visualization techniques are used to help data analysts understand the complex data. As visualization is a crucial component of the BI process, many companies see an explosion in the urgent need for it. In this article, we will talk about the top data visualization techniques and how to best use them.
A bar chart is one of the simplest data visualization techniques used by data scientists. Since human eyes can easily compare the length of bars, this chart type can be used to display complex data. These charts can be understood by everyone without any prior knowledge, explanation, or training.
The best way to best use a bar chart is to make sure not to make it too busy to compare the bars. Also, you can use different colors for bars to make the comparison easy.
Probably, everyone is familiar with a line chart. If this chart type is well-designed, people can easily analyze it with just a glance. When it comes to monitoring numeric attributes over time, no other data visualization technique could be better than a line chart.
These charts show different measures of attributes on different colored curves, making it easy for the users to compare them. Furthermore, making a line chart interactive allows users browse through curves that would be confusing otherwise in a static visualization.
For example, in the times of COVID19, the line charts are used to show the spread of the disease and see the effectiveness of the controlling measures.
A scatter plot is used to display the relative density of two sets of data points. A well-designed scatter plot correlates the complex data set in such a way that it is easy to read. This data visualization is commonly used for discovering trends and data.
They are also used to show the relationships for single data points. These diagrams are easier to read and show compared to the bar charts or other data visualization techniques. However, displaying too many points on a scatter plot makes it hard to study the data.
This data visualization technique represents data or flow of processes via lines and arrows. These arrows have different widths showing the magnitude of flows. At first, a Sankey diagram might appear to be overwhelming. But it is quite flexible and can be used for the data even with massive fluctuations.
Sankey diagram is a bit tough to understand for those who aren’t familiar with it. Therefore, you need to write explanatory notes so that the viewers can gain insights from it.
It is a data visualization technique that uses color coding to show the magnitude of two-dimensional data. For example, cloud-based companies use a heatmap to visualize the use of cloud resources at different time intervals, making it easy for the customers to figure out the right time to shut off the servers to cut down the costs. However, many technologists or data analysts find heatmap the least interesting visualization technique.
Histogram is another great data visualization type used to visualize the distribution of values in a given data set. As you know averages are misleading and can be misused. Therefore, in order to provide the viewers with more accurate information, data analysts use histograms and show the details.
Though a histogram looks like a bar graph, it is designed especially to show data distribution. Data values get grouped into equally-sized numerical ranges, called bins. Some of the values may not fall into the ranges; thus, they may not have bars. Another issue with histograms is, you need to make sure that the bins are of the right size and convey the right information.
Bubble charts are a data visualization technique used to display three-dimensional data using different-sized bubbles. With this chart type, you can provide maximum information, that too without any explanation. These bubbles are of different colors to make the data study easy.
The bubbles in this chart type should be sized in such a way that they don’t bump into each other. Moreover, the labels should be clearly visible so that they convey the insights of the data. Still, not everyone is a big fan of bubble charts as they need too much effort to understand.
Pie Charts are another useful data visualization tool on the list. While they have received some bad reputation in the last few years, they make a great data visualization tool that allows you to display important metrics is an easy-to-understand format.
They are quite useful when you want to show the proportional composition of a variable over a timeframe. Thus, this data visualization type makes a valuable thing in your arsenal.
It is a technique of data visualization that shows hierarchical data in a nested format. This chart type is the mother of all hierarchical representations of data. And, this type has also been used by human beings since years. The main idea behind using this data visualization technique is to display relations of the objects with each other.
A treemap is commonly used in organizations to find out subordinates in the management system. Also, it helps you showcase the hierarchy of items, seniority in the family trees, etc. All this plays an important role in the decision-making process. Besides, it helps you track the cause of a problem and find out the possible effects or solution.
These are some of the top data visualization techniques and the right way to use them. The type of data visualization you choose depends on your needs and type of data. Also, your audience should be familiar with the data visualization method you are going to use. If they aren’t, make sure to provide them with sufficient training so that they are able to make sense of the complex data. Hopefully, this article will help you get information about the top data visualization techniques and how to best use them.